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TGF-β inhibition can overcome cancer primary resistance to PD-1 blockade: A mathematical model
Nourridine Siewe1, Avner Friedman2
1School of Mathematical Sciences, College of Science, Rochester Institute of Technology, Rochester, New York, United States of America.
Abstract:
Immune checkpoint inhibitors have demonstrated, over the recent years, impressive clinical response in cancer patients, but some patients do not respond at all to checkpoint blockade, exhibiting primary resistance. Primary resistance to PD-1 blockade is reported to occur under conditions of immunosuppressive tumor environment, a condition caused by myeloid derived suppressor cells (MDSCs), and by T cells exclusion, due to increased level of T regulatory cells (Tregs). Since TGF-β activates Tregs, TGF-β inhibitor may overcome primary resistance to anti-PD-1. Indeed, recent mice experiments show that combining anti-PD-1 with anti-TGF-β yields significant therapeutic improvements compared to anti-TGF-β alone. The present paper introduces two cancer-specific parameters and, correspondingly, develops a mathematical model which explains how primary resistance to PD-1 blockade occurs, in terms of the two cancer-specific parameters, and how, in combination with anti-TGF-β, anti-PD-1 provides significant benefits. The model is represented by a system of partial differential equations and the simulations are in agreement with the recent mice experiments. In some cancer patients, treatment with anti-PD-1 results in rapid progression of the disease, known as hyperprogression disease (HPD). The mathematical model can also explain how this situation arises, and it predicts that HPD may be reversed by combining anti-TGF-β to anti-PD-1. The model is used to demonstrate how the two cancer-specific parameters may serve as biomarkers in predicting the efficacy of combination therapy with PD-1 and TGF-β inhibitors.
Insights
Primary resistance to PD-1 blockade in cancer can be overcome by combining PD-1 inhibitors with TGF-β inhibitors. This combination therapy also shows potential for reversing hyperprogression disease and identifying predictive biomarkers.
Area of Science:
- Immunology
- Oncology
- Mathematical Biology
Background:
- Immune checkpoint inhibitors like anti-PD-1 have shown clinical success in cancer treatment.
- However, primary resistance to anti-PD-1 therapy is a significant clinical challenge.
- This resistance is linked to an immunosuppressive tumor microenvironment, involving myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), which are influenced by TGF-β.
Purpose of the Study:
- To develop a mathematical model explaining primary resistance to PD-1 blockade.
- To investigate how combining anti-PD-1 with TGF-β inhibitors can overcome this resistance.
- To explore the model's ability to explain and predict hyperprogression disease (HPD) and its reversal.
Main Methods:
- Development of a mathematical model using a system of partial differential equations.
- Incorporation of two cancer-specific parameters into the model.
- Simulations of the mathematical model to analyze treatment responses.
Main Results:
- The mathematical model explains the mechanisms of primary resistance to PD-1 blockade.
- Simulations align with experimental findings showing therapeutic improvements with combined anti-PD-1 and anti-TGF-β therapy.
- The model predicts that combining anti-TGF-β can reverse HPD and identifies potential biomarkers for combination therapy efficacy.
Conclusions:
- A mathematical model elucidates primary resistance to PD-1 blockade and the benefits of combination therapy with TGF-β inhibitors.
- The model supports the use of combined anti-PD-1 and anti-TGF-β therapy to overcome resistance and manage HPD.
- Two cancer-specific parameters derived from the model may serve as predictive biomarkers for this combination therapy.
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